Instance Attribute Details

#data_rearrangement ⇒ String

A JSON string that represents the splitting and rearrangement
processing to be applied to a DataSource. If the
DataRearrangement parameter is not provided, all of the input data
is used to create the Datasource.

There are multiple parameters that control what data is used to
create a datasource:

percentBegin

Use percentBegin to indicate the beginning of the range of the
data used to create the Datasource. If you do not include
percentBegin and percentEnd, Amazon ML includes all of the
data when creating the datasource.

percentEnd

Use percentEnd to indicate the end of the range of the data used
to create the Datasource. If you do not include percentBegin and
percentEnd, Amazon ML includes all of the data when creating the
datasource.

complement

The complement parameter instructs Amazon ML to use the data
that is not included in the range of percentBegin to
percentEnd to create a datasource. The complement parameter is
useful if you need to create complementary datasources for
training and evaluation. To create a complementary datasource, use
the same values for percentBegin and percentEnd, along with
the complement parameter.

For example, the following two datasources do not share any data,
and can be used to train and evaluate a model. The first
datasource has 25 percent of the data, and the second one has 75
percent of the data.

To change how Amazon ML splits the data for a datasource, use the
strategy parameter.

The default value for the strategy parameter is sequential,
meaning that Amazon ML takes all of the data records between the
percentBegin and percentEnd parameters for the datasource, in
the order that the records appear in the input data.

The following two DataRearrangement lines are examples of
sequentially ordered training and evaluation datasources:

To randomly split the input data into the proportions indicated by
the percentBegin and percentEnd parameters, set the strategy
parameter to random and provide a string that is used as the
seed value for the random data splitting (for example, you can use
the S3 path to your data as the random seed string). If you choose
the random split strategy, Amazon ML assigns each row of data a
pseudo-random number between 0 and 100, and then selects the rows
that have an assigned number between percentBegin and
percentEnd. Pseudo-random numbers are assigned using both the
input seed string value and the byte offset as a seed, so changing
the data results in a different split. Any existing ordering is
preserved. The random splitting strategy ensures that variables in
the training and evaluation data are distributed similarly. It is
useful in the cases where the input data may have an implicit sort
order, which would otherwise result in training and evaluation
datasources containing non-similar data records.

The following two DataRearrangement lines are examples of
non-sequentially ordered training and evaluation datasources:

#resource_role ⇒ String

The role (DataPipelineDefaultResourceRole) assumed by an Amazon
Elastic Compute Cloud (Amazon EC2) instance to carry out the copy
operation from Amazon RDS to an Amazon S3 task. For more
information, see Role templates for data pipelines.

#security_group_ids ⇒ Array<String>

The security group IDs to be used to access a VPC-based RDS DB
instance. Ensure that there are appropriate ingress rules set up to
allow access to the RDS DB instance. This attribute is used by Data
Pipeline to carry out the copy operation from Amazon RDS to an
Amazon S3 task.

#service_role ⇒ String

The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline
service to monitor the progress of the copy task from Amazon RDS to
Amazon S3. For more information, see Role templates for data
pipelines.